Bibliographic record
Abstract
shaping est un outil Web qui permet de manipuler directement l’espace cartographique afin de sculpter, de retrancher, d’étendre et de contracter les régions d’une carte. En rupture avec la compréhension euclidienne rigide de l’espace projeté qui caractérise les systèmes d’information géographique (SIG), ces opérations permettent un travail de cartographie créative dans lequel l’espace est fluide, dynamique, relationnel et situé. Chaque opération est décrite en détail, accompagnée d’usages possibles suggérés par des textes sur la géographie et la cartographie. La plupart des manipulations de l’espace que permet shaping se traduisent en langage QGIS, ce qui permet la transformation des vecteurs et des couches de rasters de l’information géographique. En permettant la manipulation directe en temps réel de l’espace cartographique, shaping sert d’outil à l’expressivité appliquée à l’information géographique. C’est aussi un exemple de la manière dont on peut concevoir des outils accessibles qui, tout en étant compatibles avec les SIG existants, conservent leur propre utilité.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".